Exponential Models in Language Modeling and Machine Learning

Abstract

Random fields and exponential models are well-studied and widely used
methods in statistics, physics, computer vision, and other areas of
computational science. In this talk we present new uses of and results
related to this family of models, motivated from problems in natural
language processing and machine learning. First, for segmenting and
labeling sequences, we present a framework based on conditional random
fields, which offers several advantages over hidden Markov models and
stochastic grammars, the most commonly used tools for such tasks.
Second, we derive an equivalence between the well-known technique of
boosting in machine learning and maximum likelihood for exponential
models. In both cases, the idea of using unnormalized models plays
a key role.

About the Speaker

John Lafferty is an Associate Professor at Carnegie Mellon University,
where he holds a joint appointment in the Computer Science Department,
the Language Technologies Institute, and the Center for Automated
Learning and Discovery. He has been on the CMU faculty since 1994.
Prior to joining CMU, Dr. Lafferty was a Research Staff Member at the
IBM Thomas J. Watson Research Center in Yorktown Heights, working on
statistical methods for language processing. Dr. Lafferty received
the Ph.D. in Mathematics from Princeton University, where he was a
also member of the Program in Applied and Computational Mathematics.
He is a past editorial board member of Computational Linguistics, and
is currently an action editor for the Journal of Machine Learning
Research, and the Machine Learning journal. His research interests
include statistical learning algorithms, natural language processing,
information retrieval, and coding and information theory.